The 2-vertex-connectivity augmentation problem for specified vertices (2VCA-SV) is defined as follows: Given an undirected graph G=(V,E), a subgraph G0=(V,E') of G, a specified set of vertices S V and a weight function w:E R^+ (nonnegative real numbers), find a set E" E-E' with the minimum total weight, such that G0+E"=(V,E' E") has at least two internally disjoint paths between any pair of vertices in S. In this paper, we propose an O(|V||E|+ |V|2 log |V|) time algorithm 2-ABIS, whose performance ratio is 2 (3, respectively), for 2VCA-SV if G0 has a connected component containing S (otherwise).

GPS receivers are susceptible to jamming by interference. This paper proposes a recurrent neural network (RNN) predictor for new application in GPS anti-jamming systems. Five types of narrowband jammers, i. e. AR process, continuous wave interference (CWI), multi-tone CWI, swept CWI, and pulsed CWI, are considered in order to emulate realistic conditions. As the observation noise of received signals is highly non-Gaussian, an RNN estimator with a nonlinear structure is employed to accurately predict the narrowband signals based on a real-time learning method. The node decoupled extended Kalman filter (NDEKF) algorithm is adopted to achieve better performance in terms of convergence rate and quality of solution while requiring less computation time and memory. We analyze the computational complexity and memory requirements of the NDEKF approach and compare them to the global extended Kalman filter (GEKF) training paradigm. Simulation results show that our proposed scheme achieves a superior performance to conventional linear/nonlinear predictors in terms of SNR improvement and mean squared prediction error (MSPE) while providing inherent protection against a broad class of interference environments.

In this letter we propose a new fast matching algorithm that has no degradation of predicted images such as found in the conventional full search (FS) algorithm, so as to reduce the amount of computation of the FS algorithm for motion estimation in real-time video coding applications. That is, our proposing algorithm reduces only unnecessary computations in the process of motion estimation without decreasing the prediction quality compared to the conventional FS algorithm. The computational reduction comes from rapid elimination of impossible motion vectors. In comparison to the FS algorithm, we obtained faster elimination of inappropriate candidate motion vectors using efficient matching units based on image complexity. Experimentally, we demonstrated that the unnecessary computations were removed by about 30% as compared to the other fast FS algorithms.

Based on a Quantum-inspired Evolutionary Algorithm (QEA), a new disk allocation method is proposed for distributing buckets of a binary cartesian product file among unrestricted number of disks to maximize concurrent disk I/O. It manages the probability distribution matrix to represent the qualities of the genes. Determining the excellent genes quickly makes the proposed method have faster convergence than DAGA. It gives better solutions and 3.2 - 11.3 times faster convergence than DAGA.

A software module for the three-dimensional simulation of etching processes has been developed. It works on multilayer structures given as triangulated surface meshes. The mesh is moved nodewise according to rates which, in this work, have been determined from isotropic and anisotropic components. An important feature of the algorithm is the automatic detection of triple lines along mask edges and the refinement of triangles at these triple lines. This allows for the simulation of underetching. The capabilities of the algorithm are demonstrated by several examples such as the simulation of glass etching for the fabrication of a phase shift mask for optical lithography and the etching of an STI trench structure. Moreover, etch profiles of a silicon substrate covered by an oxide mask are shown for different parameters of the etch components. Spacer etching has also been performed. Furthermore, a specific algorithm for the simulation of purely isotropic etching is described and demonstrated.

A novel fast calculation algorithm (FCA) for calculating the decision metric of the multiple-symbol differential detection (MSDD) considering the autocorrelation of a received sequence is proposed. In correspondence to the star quadrature amplitude modulation (QAM), the M algorithm is adopted to MSDD over Rayleigh fading channels, in order to reduce the number of search paths. The computational complexity of the decision metric can be greatly reduced by the proposed FCA and the M algorithm. Through computer simulations, it is confirmed that the symbol error rate (SER) performance of the MSDD considering autocorrelation is closer to that of the ideal coherent detection as the length of an observed sequence becomes larger over Rayleigh fading channels.

An adaptive array antenna for the suppression of high-power interference in direct-sequence code-division multiple access (DS-CDMA) systems is presented. Although DS-CDMA has sufficient flexibility to support a variety of services, from voice to moving-pictures, with high levels of quality, multiple access interference (MAI) is a problem. This is particularly so of the high-power interference which accompanies high-speed transmission in DS-CDMA. While the application of adaptive array antennas is an effective way of improving signal-to-interference-plus-noise ratio (SINR), problems with this approach include large levels of power consumption and the high costs of hardware and of implementing the antennas. Therefore, our main purpose is to realize a simple configuration for an adaptive array system. In order to reduce the required amounts of processing, a common beam provides suppression of high-power interference for the low-bit-rate users; this makes per-user preparation of weights unnecessary. This approach also reduces the consumption of power by the system. Interference is cancelled by minimization of the array output power (i.e., the application of a power inversion algorithm) before despreading. The approach also allows us to improve the implementation of the antenna elements by using small auxiliary antennas. The basic performance of the system is confirmed through numerical calculation and computer simulation. Furthermore, a real-time processing unit has been developed and the effectiveness of the approach is confirmed by an experiment in a radio-anechoic chamber.

This paper presents a new hierarchical scheduling method for a large-scale manufacturing system based on the hybrid Petri-net model, which consists of CPN (Continuous Petri Net) and TPN (Timed Petri Net). The study focuses on an automobile production system, a typical large-scale manufacturing system. At a high level, CPN is used to represent continuous flow in the production process of an entire system, and LP (Linear Programming) is applied to find the optimal flow. At a low level, TPN is used to represent the manufacturing environment of each sub-production line in a decentralized manner, and the MCT algorithm is applied to find feasible semi-optimal process sequences for each sub-production line. Our proposed scheduling method can schedule macroscopically the flow of an entire system while considering microscopically any physical constraints that arise on an actual shop floor.

The wavelet transform (WT) has recently emerged as a powerful tool for image compression. In this paper, a new image compression technique combining the genetic algorithm (GA) and grey-based competitive learning network (GCLN) in the wavelet transform domain is proposed. In the GCLN, the grey theory is applied to a two-layer modified competitive learning network in order to generate optimal solution for VQ. In accordance with the degree of similarity measure between training vectors and codevectors, the grey relational analysis is used to measure the relationship degree among them. The GA is used in an attempt to optimize a specified objective function related to vector quantizer design. The physical processes of competition, selection and reproduction operating in populations are adopted in combination with GCLN to produce a superior genetic grey-based competitive learning network (GGCLN) for codebook design in image compression. The experimental results show that a promising codebook can be obtained using the proposed GGCLN and GGCLN with wavelet decomposition.

In this letter, base station placement is automatically determined from pre-defined candidate sites using a genetic approach, and the transmit power is obtained taking the interference situation into account in cases of interference-dominant systems. In order to apply a genetic algorithm to the base station placement problem, a real-valued representation scheme is proposed. Corresponding operators such as crossover and mutation are also introduced. The proposed algorithm is applied to an inhomogeneous traffic density environment, where a base station's coverage may be limited by offered traffic loads. An objective function is designed for performing the cell planning in a coverage- and cost-effective manner.

The ATM-ABR service category provides minimum cell rate (MCR) guarantees and robust connections even with insufficient network resources. Recently proposed rate-management algorithms for supporting multimedia applications over ABR mainly aim at minimizing the cell loss and delay. However, jitter is also an important element of QoS for multimedia applications. In this paper, we focus our attention on the arrival point of the critical cell corresponding to the end of data packet and propose a simple cell scheduling scheme for source node to reduce the jitter on application level over the ATM-ABR service class. In our proposed method, critical cells are delayed intentionally and the packet stream at application level becomes smooth. We verify the effectiveness of our proposed algorithm by an analytical model and simulation. From those results, we find that our proposed scheduling algorithm is effective in reducing the application level jitter even when the tagged cell stream is transmitted along the path with multiple nodes.

This paper describes a stock trading system based on reinforcement learning, regarding the process of stock price changes as Markov decision process (MDP). The system adopts two popular reinforcement learning algorithms, temporal-difference (TD) and Q, for selecting stocks and optimizing trading parameters, respectively. Input features of the system are devised using technical analysis and value functions are approximated by feedforward neural networks. Multiple cooperative agents are used for Q-learning to efficiently integrate global trend prediction with local trading strategy. Agents communicate with others sharing training episodes and learned policies, while keeping the overall scheme of conventional Q-learning. Experimental results on the Korean stock market show that our trading system outperforms the market average and makes appreciable profits. Furthermore, we can find that our system is superior to a system trained by supervised learning in view of risk management.

Many services on the Internet are provided by multiple identical servers in order to improve performance and robustness. The number, the location and the distribution of servers affect the performance and reliability of a service. The server placement is, however, often determined based on the empirical knowledge of the administrators. This paper investigates issues of the server placement in terms of the service performance and the server load. We identify that a server selection mechanism plays an important role in server placement, and thus, evaluate different server selection algorithms. The result shows that it is essential to the robustness of a service to employ a mechanism which distributes service requests to the servers according to the measured response time of each server. As a case study, we evaluate the server selection mechanisms employed by different DNS (Domain Name System) implementations. Then, we show the effects of the different server selection algorithms using root-server measurements taken at different locations around the world.

Digital halftoning is a technique to convert a continuous-tone image into a binary image consisting of black and white dots. It is an important technique for printing machines and printers to output an image with few intensity levels or colors which looks similar to an input image. This paper surveys how algorithm engineering can contribute to digital halftoning or what combinatorial problems are related to digital halftoning. A common criterion on optimal digital halftoning leads to a negative result that obtaining an optimal halftoned image is NP-complete. So, there are two choices: approximation algorithm and polynomial-time algorithm with relaxed condition. Main algorithmic notions related are geometric discrepancy, matrix (or array) rounding problems, and network-flow algorithms.

We consider the routing for a multicast in a WDM all-optical network. We prove a min-max theorem on the number of wavelengths necessary for routing a multicast. Based on the min-max theorem, we propose an efficient on-line algorithm for routing a multicast.

Let each vertex v of a graph G have a positive integer weight ω(v). Then a multicoloring of G is to assign each vertex v a set of ω(v) colors so that any pair of adjacent vertices receive disjoint sets of colors. A partial k-tree is a graph with tree-width bounded by a fixed constant k. This paper presents an algorithm which finds a multicoloring of any given partial k-tree G with the minimum number of colors. The computation time of the algorithm is bounded by a polynomial in the number of vertices and the maximum weight of vertices in G.

Assume that each edge e of a graph G is assigned a list (set) L(e) of colors. Then an edge-coloring of G is called an L-edge-coloring if each edge e of G is colored with a color contained in L(e). It is known that any series-parallel simple graph G has an L-edge-coloring if either (i) |L(e)| max{4,d(v),d(w)} for each edge e=vw or (ii) the maximum degree of G is at most three and |L(e)| 3 for each edge e, where d(v) and d(w) are the degrees of the ends v and w of e, respectively. In this paper we give a linear-time algorithm for finding such an L-edge-coloring of a series-parallel graph G.

We propose two simple algorithms based on bounded tickets for the mutual exclusion problem on the asynchronous single-writer/multi-reader shared memory model. These algorithms are modifications of the Bakery algorithm. An unattractive property of the Bakery algorithm is that the size of its shared memory is unbounded. Initially we design a provisional algorithm based on bounded tickets. It guarantees mutual exclusion in the case where a certain condition is satisfied. To remove the condition, we use an additional process that does not correspond to any user. The algorithm with the additional process is a lockout-free mutual exclusion algorithm on the asynchronous single-writer/multi-reader shared memory model. We then modify this algorithm to reduce the shared memory size with the cost of using another additional process. The maximum waiting time using each of the algorithms proposed in this paper is bounded by (n-1)c+O(nl), where n is the number of users, l is an upper bound on the time between two successive atomic steps, and c is an upper bound on the time that any user spends using the resource. The shared memory size needed by the first algorithm and the second algorithm are (n+1)(1+log (4n)) bits and n(1+log (4n-4))+2 bits, respectively.

It is known that all minimum cuts in an edge-weighted undirected graph with n vertices and m edges can be represented by a cactus with O(n) vertices and edges, a connected graph in which each edge is contained in an exactly one cycle. In this paper, we show that such a cactus representation can be computed in O(mn+n2log n) time and O(m) space. This improves the previously best complexity of deterministic cactus construction algorithms, and matches with the time bound of the fastest deterministic algorithm for computing a single minimum cut.

The radiation patterns are synthesized by properly disposing surface variations on dielectric rod waveguides. The genetic algorithm (GA) is applied for searching the optimum disposition of variation sections. A very fast calculation method used in the optimization is presented. The guided waves are related in the form of a 2-port circuit and the radiation field is expressed by superposition of the waves from variation sections. Various conical beams can be synthesized. Short variation sections and combination of several variation sections with different height are used to improve the synthesis performance. The ripple of the mainlobe and the sidelobe levels become small. Spherical sector patterns with a steep fall are synthesized and the agreement with the experimental values is confirmed.